Energy-aware and carbon-efficient VM placement optimization in cloud datacenters using evolutionary computing methods

One of the most critical concerns cloud service providers is balancing renewable and fossil energy consumption. On other hand, policy organizations governments to reduce consumption greenhouse gas emissions in data centers. Recently, a lot research has been conducted optimize virtual machine placement on physical machines minimize Many previous studies have not considered deadline scheduling Internet Things (IoT) tasks. Therefore, modelings are mainly well-suited IoT environments where requests time-constraint. Unfortunately, both sub-problems minimization fall into NP-hard issues. This study proposes multi-objective jointly costs scheduling. After presenting modified Memetic algorithm, we compare its performance with baseline methods state-of-the-art ones. The simulation results CloudSim platform show that proposed method can costs, carbon footprints, service-level agreement violations, total response time requests.